The Determinants of Industry Political Activity, 1978–1986

1994 ◽  
Vol 88 (4) ◽  
pp. 911-926 ◽  
Author(s):  
Kevin B. Grier ◽  
Michael C. Munger ◽  
Brian E. Roberts
Keyword(s):  
Political Action ◽  
Cost Model ◽  
Large Data ◽  
Political Activity ◽  
Small Samples ◽  
Political Action Committees ◽  
Data Set ◽  
Collective Action Problems ◽  
Potential Benefits ◽  
Benefit Cost

While the allocation of interest group monies to specific politicians has been extensively studied, little is known about the factors that determine of the overall level of political activity across groups. We study total contributions by corporate political action committees at the industry level. We create a large data set on industry political activity, covering 124 industries across five election cycles from 1978 to 1986 and sketch out a simple benefit-cost model to predict total corporate PAC contributions in each industry. The few previous studies of this phenomenon use relatively small samples and employ statistical techniques that are either biased or impose untested restrictions. The selectivity-corrected regression technique used here solves these problems. We find that industries with greater potential benefits from government assistance contribute systematically more but that the ability to realize these benefits is constrained by collective action problems facing firms in each industry.

Disaggregating and Explaining Corporate Political Activity: Domestic and Foreign Corporations in National Politics

2000 ◽  
Vol 94 (4) ◽  
pp. 891-903 ◽  
Author(s):  
Wendy L. Hansen ◽  
Neil J. Mitchell
Keyword(s):  
Charitable Giving ◽  
Strategic Behavior ◽  
Political Action ◽  
Political Activity ◽  
Political Action Committees ◽  
National Politics ◽  
Corporate Political Activity ◽  
Political Activities ◽  
Empirical Level ◽  
Foreign Corporations

Corporate political activity is usually operationalized and analyzed as financial contributions to candidates or political parties through political action committees (PACs). Very little attention has been paid to other dimensions, such as lobbying, in a systematic way. On a theoretical level we address the issue of how to conceive of PAC contributions, lobbying, and other corporate activities, such as charitable giving, in terms of the strategic behavior of corporations and the implications of “foreignness” for the different types of corporate political activity. On an empirical level we examine the political activities of Fortune 500 firms, along with an oversampling of U.S. affiliates of large foreign investors for the 1987–88 election cycle.

Corporate Political Strategies

1990 ◽  
Vol 20 (2) ◽  
pp. 281-288 ◽  
Author(s):  
Graham K. Wilson
Keyword(s):  
Political Action ◽  
Empirical Studies ◽  
Political Activity ◽  
Political Action Committees ◽  
Political Contributions ◽  
Corporate Political Activity ◽  
Political Behaviour ◽  
Corporate Political Strategies ◽  
Ready Availability ◽  
Politically Active

With one partial exception, political scientists have carried out little empirical research on corporate political activity. That one exception is political action committees, PACs. Perhaps because of the ready availability of apparently reliable data on corporate political contributions, most empirical studies of business political activity have concentrated on PACs. The study of PACs is not, however, synonymous with the study of corporate political behaviour. Indeed, not all corporations have PACs; Sabato estimated that almost half the largest manufacturing corporations did not. At least one politically active corporation, Du Pont, refused for many years to establish a PAC.

The Corporate Context of Lobbying Activity

2006 ◽  
Vol 8 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Holly Brasher ◽  
David Lowery
Keyword(s):  
Political Action ◽  
Political Activity ◽  
Empirical Literature ◽  
Political Action Committees ◽  
Prior Work ◽  
Corporate Political Activity ◽  
Business Context ◽  
Economic Context ◽  
Corporate Context ◽  
Size Matter

Despite extensive research on political activity on the part of corporations, clear and consistent findings remain elusive. We identify three reasons for this failure. First, most of the empirical literature on corporate political activity simply studies the wrong phenomena by examining political action committees rather than lobbying more generally. Second, the literature studies an excessively narrow sample of organizations that might engage in lobbying, focusing almost always on extremely large corporations, which inevitably attenuates variance on many of the variables hypothesized to influence engagement in political activity. And third, prior work is rarely attentive to the diversity of corporate activities, narrowly conceptualizing vital aspects of the business context that might influence decisions to engage in political activity. Based on this critique, we develop and test new models of corporate political activity, finding that the diversity of the economic context within which firms work and firm size matter a great deal, if in ways somewhat different from those reported in prior work.

MODEL SELECTION AND AVERAGING OF HEALTH COSTS IN EPISODE TREATMENT GROUPS

2016 ◽  
Vol 47 (1) ◽  
pp. 153-167 ◽  
Author(s):  
Shujuan Huang ◽  
Brian Hartman ◽  
Vytaras Brazauskas
Keyword(s):  
Random Forest ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Cost Model ◽  
Model Averaging ◽  
Large Data ◽  
Data Set ◽  
Treatment Groups ◽  
Wide Range

Episode Treatment Groups (ETGs) classify related services into medically relevant and distinct units describing an episode of care. Proper model selection for those ETG-based costs is essential to adequately price and manage health insurance risks. The optimal claim cost model (or model probabilities) can vary depending on the disease. We compare four potential models (lognormal, gamma, log-skew-t and Lomax) using four different model selection methods (AIC and BIC weights, Random Forest feature classification and Bayesian model averaging) on 320 ETGs. Using the data from a major health insurer, which consists of more than 33 million observations from 9 million claimants, we compare the various methods on both speed and precision, and also examine the wide range of selected models for the different ETGs. Several case studies are provided for illustration. It is found that Random Forest feature selection is computationally efficient and sufficiently accurate, hence being preferred in this large data set. When feasible (on smaller data sets), Bayesian model averaging is preferred because of the posterior model probabilities.

scholarly journals Prolific Inventor Productivity and Mobility: A Western/Asian com-parison. Evidence from US Patent Data for 12 Countries

2013 ◽  
Vol 15 (4) ◽  
pp. 117-132
Author(s):  
Christian Le Bas ◽  
William Latham ◽  
Dmitry Volodin
Keyword(s):  
Empirical Evidence ◽  
Creative Process ◽  
Economic Value ◽  
Innovation Process ◽  
Large Data ◽  
Small Samples ◽  
Patent Data ◽  
Data Set ◽  
Seminal Work

This paper provides new insights into the role of individual inventors in the innovation process. Individuals are central in this creative process because innovation is not simply a product of firms and organizations; it requires individual creativity (Rothaermel and Hess, 2007). We focus our analysis on prolific inventors (a rich sub category of inventors) because they contribute so hugely to national invention totals (Le Bas et al., 2010) and tend to produce inventions that have more economic value (Gambardella et al., 2005; Gay et al., 2008). Converging empirical evidence has established the significance of prolific inventors (Ernst et al., 2000). Previous studies of prolific (or “key”) inventors have focused more on the firms in which they work or on the industries in which the firms operate. Narin and Breitzman’s (1995) seminal work on the topic is based on an analysis of only four firms in a single sector and a recent paper by Pilkington et al. (2009) uses only two firms. In contrast to these studies on small samples, we use a very large data set which includes thousands of inventors in thousands of firms from several countries.

scholarly journals Local Union Officers' Donations to a Political Action Committee

2005 ◽  
Vol 51 (1) ◽  
pp. 40-61 ◽  
Author(s):  
Marick F. Masters ◽  
Robert S. Atkin
Keyword(s):  
Political Action ◽  
The United States ◽  
Political Activity ◽  
Political Action Committees ◽  
Political Action Committee ◽  
Union Commitment ◽  
Strategic Response ◽  
Presidential Candidates ◽  
Local Union ◽  
Action Committee

During the 1980s, unions in the United States significantly increased their political activity, partly as a strategic response to declining membership. An important aspect of this effort is contributing money to congressional and presidential candidates through political action committees (PACs). U.S. federal election campaign laws allow unions to raise PAC money from members on a strictly uoluntary basis. Elected local union officers may play an important part in union PAC fundraising, as they are a sizable cadre of potential donors and their donations may send powerful signais to rank-and-file to donate as well. This paper examines the PAC donations among a sample of elected local union officers of the United Steelworkers of America (USW). The descriptive results show significant variation in officers' PAC donations. Regression analyses show that union commitment is a significant predictor of PAC support as is location in a non-right-to-work state. The results have implications for promoting union PAC fundraising efforts, and hence the potential of U.S. unions to rely on political action as a strategy for resurgence.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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